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<title>Online appendix for “Pandemic Pass? Treaty Derogations and Human Rights Practices During COVID-19”</title>
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  <li><a href="#modeling-approach" id="toc-modeling-approach" class="nav-link active" data-scroll-target="#modeling-approach">Modeling approach</a>
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  <li><a href="#priors" id="toc-priors" class="nav-link" data-scroll-target="#priors">Priors</a></li>
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<h1 class="title">Online appendix for “Pandemic Pass? Treaty Derogations and Human Rights Practices During COVID-19”</h1>
<p class="subtitle lead">(Research note)</p>
</div>


<div class="quarto-title-meta-author">
  <div class="quarto-title-meta-heading">Authors</div>
  <div class="quarto-title-meta-heading">Affiliations</div>
  
    <div class="quarto-title-meta-contents">
    <p class="author"><a href="https://www.suparnachaudhry.com/">Suparna Chaudhry</a> <a href="mailto:schaudhry@lclark.edu" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0003-3506-7590" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Lewis and Clark College
          </p>
      </div>
    <div class="quarto-title-meta-contents">
    <p class="author"><a href="https://audreylcomstock.weebly.com/">Audrey L. Comstock</a> <a href="mailto:audrey.comstock@asu.edu" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0003-1772-5350" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Arizona State University
          </p>
      </div>
    <div class="quarto-title-meta-contents">
    <p class="author"><a href="https://www.andrewheiss.com">Andrew Heiss</a> <a href="mailto:aheiss@gsu.edu" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0002-3948-3914" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Georgia State University
          </p>
      </div>
  </div>

<div class="quarto-title-meta">

      
    <div>
    <div class="quarto-title-meta-heading">Latest version</div>
    <div class="quarto-title-meta-contents">
      <p class="date">July 25, 2024</p>
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</header>


<section id="modeling-approach" class="level2">
<h2 class="anchored" data-anchor-id="modeling-approach">Modeling approach</h2>
<p>We use Stan 2.35.0 <span class="citation" data-cites="stan">(<a href="#ref-stan" role="doc-biblioref">Stan Development Team 2023</a>)</span> through R 4.4.0 <span class="citation" data-cites="rproject-4-3-3">(<a href="#ref-rproject-4-3-3" role="doc-biblioref">R Core Team 2023</a>)</span> and {brms} 2.21.0 <span class="citation" data-cites="Burkner:2017">(<a href="#ref-Burkner:2017" role="doc-biblioref">Bürkner 2017</a>)</span> to estimate our models. We generate 4 MCMC chains for each model with 2,000 iterations in each chain, 1,000 of which are used for warmup. All chains converge; we assess convergence with visual inspection.</p>
<p>Complete results from all the models, along with posterior predictive checks, goodness-of-fit measures, and prediction diagnostics are all available at a companion statistical analysis compendium at <a href="https://doi.org/10.17605/OSF.IO/7GVDA" class="uri">https://doi.org/10.17605/OSF.IO/7GVDA</a>.</p>
<section id="priors" class="level3">
<h3 class="anchored" data-anchor-id="priors">Priors</h3>
<p>We follow the suggestion of <span class="citation" data-cites="GelmanJakulinPittau:2008">Gelman et al. (<a href="#ref-GelmanJakulinPittau:2008" role="doc-biblioref">2008</a>)</span> and use weakly informative priors for our logistic and ordered logistic regression models. For consistency with prior specification, and for computation efficiency, we mean-center all nonbinary variables so that parameter estimates represent changes from the mean. We use two general priors (see <a href="#fig-priors" class="quarto-xref">Figure&nbsp;1</a>):</p>
<ul>
<li>For all <span class="math inline">\(\beta\)</span> terms, we use a Student t distribution with a mean of 0 and a standard deviation of 3. This keeps most parameter estimates around −5 to 5, with thicker tails that allow for some possibility of extreme values.</li>
<li>For <span class="math inline">\(\sigma\)</span> terms related to the variance or standard deviation of parameter distributions, which must be positive, we use a half Cauchy distribution, centered at 0 with a <span class="math inline">\(\gamma\)</span> of 1</li>
</ul>
<p>These priors give more weight to realistic areas of parameter values and downweight values in unrealistic spaces. For instance, since logit-scale coefficient values greater than 4 or 5 are highly unlikely, our Student t prior puts more weight on smaller values. Additionally, weakly informative priors allow reasonable and considerable uncertainty in possible parameter estimates.</p>
<div class="cell" data-layout-align="center">
<div class="cell-output-display">
<div id="fig-priors" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-align="center">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-priors-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="appendix_files/figure-html/fig-priors-1.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:100.0%">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-priors-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;A&nbsp;1: Density plots of prior distributions for model parameters
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="model-definitions" class="level3">
<h3 class="anchored" data-anchor-id="model-definitions">Model definitions</h3>
<section id="h1-logistic-regression" class="level4">
<h4 class="anchored" data-anchor-id="h1-logistic-regression">H<sub>1</sub>: Logistic regression</h4>
<div class="cell" data-layout-align="center" data-extra.preamble="[&quot;\usepackage{libertine}&quot;,&quot;\usepackage{libertinust1math}&quot;]">
<div class="cell-output-display">
<div id="fig-country-week-structure" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-align="center">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-country-week-structure-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="appendix_files/figure-html/fig-country-week-structure-1.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:100.0%">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-country-week-structure-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;A&nbsp;2: Hierarchy of country-week data, showing location and frequency of measured variables
</figcaption>
</figure>
</div>
</div>
</div>
<p><span class="math display">\[
\begin{aligned}
&amp;\ \mathrlap{\textbf{Binary outcome $i$ across week $t$ within each country $j$}} \\
\text{Outcome}_{it_j} \sim&amp;\ \operatorname{Bernoulli}(\pi_{it_j}) \\[0.75em]
&amp;\ \textbf{Distribution parameters} \\
\pi_{it_j} =&amp;\ (\beta_0 + b_{0_j}) + \beta_1 \text{Derogation in effect}_{it} + \\
&amp;\ \beta_2\ \text{New cases}_{it}\ + \beta_3\ \text{Cumulative cases}_{it}\ + \\
&amp;\ \beta_4\ \text{New deaths}_{it}\ + \beta_5\ \text{Cumulative deaths}_{it}\ + \\
&amp;\ \beta_6\ \text{Past ICCPR derogation}_{it}\ + \beta_7\ \text{Past ICCPR action}_{it}\ + \\
&amp;\ \beta_8\ \text{Rule of law index}_{it}\ + \beta_9\ \text{Civil liberties index}_{it}\ + \\
&amp;\ \beta_{10}\ \text{Core civil society index}_{it}\ + \beta_{11}\ \text{Week number}_{it} \\
b_{0_j} \sim&amp;\ \mathcal{N}(0, \sigma_0) \\[0.75em]
&amp;\ \textbf{Priors} \\
\beta_{0 \dots 11} \sim&amp;\ \operatorname{Student\ t}(\nu = 1, \mu = 0, \sigma = 3) \\
\sigma_0 \sim&amp;\ \operatorname{Cauchy}(x = 0, \gamma = 1) \text{, lower bound} = 0
\end{aligned}
\]</span></p>
<p>The actual R code for these models is included in the replication code at <a href="https://doi.org/10.17605/OSF.IO/ANONYMIZED-FOR-NOW" class="uri">https://doi.org/10.17605/OSF.IO/ANONYMIZED-FOR-NOW</a> This is a simplified representation of the {brms} <span class="citation" data-cites="Burkner:2017">(<a href="#ref-Burkner:2017" role="doc-biblioref">Bürkner 2017</a>)</span> model code:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># H1: Logistic regression for binary outcomes</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">brm</span>(</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bf</span>(outcome <span class="sc">~</span> derogation_ineffect <span class="sc">+</span> </span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>      new_cases_z <span class="sc">+</span> cumulative_cases_z <span class="sc">+</span> </span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>      new_deaths_z <span class="sc">+</span> cumulative_deaths_z <span class="sc">+</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>      prior_iccpr_derogations <span class="sc">+</span> prior_iccpr_other_action <span class="sc">+</span> </span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>      v2x_rule <span class="sc">+</span> v2x_civlib <span class="sc">+</span> v2xcs_ccsi <span class="sc">+</span> </span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>      year_week_num <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> country_name)),</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">bernoulli</span>(),</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">prior =</span> <span class="fu">c</span>(</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">student_t</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">3</span>), <span class="at">class =</span> Intercept),</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">student_t</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">3</span>), <span class="at">class =</span> b),</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">cauchy</span>(<span class="dv">0</span>, <span class="dv">1</span>), <span class="at">class =</span> sd, <span class="at">lb =</span> <span class="dv">0</span>)),</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>  ...</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
<section id="h2-ordered-logistic-regression" class="level4">
<h4 class="anchored" data-anchor-id="h2-ordered-logistic-regression">H<sub>2</sub>: Ordered logistic regression</h4>
<div class="cell" data-layout-align="center" data-extra.preamble="[&quot;\usepackage{libertine}&quot;,&quot;\usepackage{libertinust1math}&quot;]">
<div class="cell-output-display">
<div id="fig-region-quarter-structure" class="quarto-figure quarto-figure-center quarto-float anchored" data-fig-align="center">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-region-quarter-structure-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img src="appendix_files/figure-html/fig-region-quarter-structure-1.svg" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:100.0%">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-region-quarter-structure-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure&nbsp;A&nbsp;3: Hierarchy of region-quarter data, showing location and frequency of measured variables
</figcaption>
</figure>
</div>
</div>
</div>
<p><span class="math display">\[
\begin{aligned}
&amp;\ \mathrlap{\textbf{Model of outcome level $i$ across quarter $t$ within each region $j$}} \\
\text{Outcome}_{it_j} \sim&amp;\ \operatorname{Ordered\ logit}(\phi_{it_j}, \alpha_k) \\[0.75em]
&amp;\ \textbf{Models for distribution parameters} \\
\phi_{it_j} =&amp;\ (\beta_0 + b_{0_j}) + \beta_1 \text{Derogation in effect}_{it} + \\
&amp;\ \beta_2\ \text{New cases}_{it}\ + \beta_3\ \text{Cumulative cases}_{it}\ + \\
&amp;\ \beta_4\ \text{New deaths}_{it}\ + \beta_5\ \text{Cumulative deaths}_{it}\ + \\
&amp;\ \beta_6\ \text{Past ICCPR derogation}_{it}\ + \beta_7\ \text{Past ICCPR action}_{it}\ + \\
&amp;\ \beta_8\ \text{Rule of law index}_{it}\ + \beta_9\ \text{Civil liberties index}_{it}\ + \\
&amp;\ \beta_{10}\ \text{Core civil society index}_{it}\ + \beta_{11}\ \text{Quarter number}_{it} \\
b_{0_j} \sim&amp;\ \mathcal{N}(0, \sigma_0) \\[0.75em]
&amp;\ \textbf{Priors} \\
\beta_{0 \dots 11} \sim&amp;\ \operatorname{Student\ t}(\nu = 1, \mu = 0, \sigma = 3) \\
\sigma_0 \sim&amp;\ \operatorname{Cauchy}(x = 0, \gamma = 1) \text{, lower bound} = 0 \\
\alpha_k \sim&amp;\ \mathcal{N}(0, 1)
\end{aligned}
\]</span></p>
<p>The actual R code for these models is included in the replication code at <a href="https://doi.org/10.17605/OSF.IO/ANONYMIZED-FOR-NOW" class="uri">https://doi.org/10.17605/OSF.IO/ANONYMIZED-FOR-NOW</a> This is a simplified representation of the {brms} <span class="citation" data-cites="Burkner:2017">(<a href="#ref-Burkner:2017" role="doc-biblioref">Bürkner 2017</a>)</span> model code:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># H2: Ordinal logistic regression for ordered outcomes</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">brm</span>(</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">bf</span>(outcome <span class="sc">~</span> derogation_ineffect <span class="sc">+</span> </span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>      new_cases_z <span class="sc">+</span> cumulative_cases_z <span class="sc">+</span> </span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>      new_deaths_z <span class="sc">+</span> cumulative_deaths_z <span class="sc">+</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>      prior_iccpr_derogations <span class="sc">+</span> prior_iccpr_other_action <span class="sc">+</span> </span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>      v2x_rule <span class="sc">+</span> v2x_civlib <span class="sc">+</span> v2xcs_ccsi <span class="sc">+</span> </span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>      year_quarter_num <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> who_region)),</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">cumulative</span>(),</span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">prior =</span> <span class="fu">c</span>(</span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">student_t</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">3</span>), <span class="at">class =</span> Intercept),</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">student_t</span>(<span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">3</span>), <span class="at">class =</span> b),</span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a>    <span class="fu">prior</span>(<span class="fu">cauchy</span>(<span class="dv">0</span>, <span class="dv">1</span>), <span class="at">class =</span> sd, <span class="at">lb =</span> <span class="dv">0</span>)),</span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a>  ...</span>
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</section>
</section>
</section>
<section id="h1-derogations-and-emergency-policies" class="level2">
<h2 class="anchored" data-anchor-id="h1-derogations-and-emergency-policies">H<sub>1</sub>: Derogations and emergency policies</h2>
<section id="predicted-probabilities-and-group-contrasts" class="level3">
<h3 class="anchored" data-anchor-id="predicted-probabilities-and-group-contrasts">Predicted probabilities and group contrasts</h3>
<div class="cell" data-layout-align="center">
<div id="tbl-policies-summary" class="cell quarto-float anchored" data-layout-align="center">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-policies-summary-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;A&nbsp;1: Summary of predicted probabilities and minimum and maximum contrasts between derogating and non-derogating countries for emergency policy models
</figcaption>
<div aria-describedby="tbl-policies-summary-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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        <thead>
<tr>
<th scope="col" align="center" colspan="1"> </th>
<th scope="col" align="center" colspan="2">Predicted probabilities</th>
<th scope="col" align="center" colspan="3">Smallest difference</th>
<th scope="col" align="center" colspan="3">Largest difference</th>
</tr>
        
              <tr>
                <th scope="col">Derogation</th>
                <th scope="col">March 2020</th>
                <th scope="col">June 2021</th>
                <th scope="col">Week</th>
                <th scope="col">\(\Delta\)</th>
                <th scope="col">p &gt; 0</th>
                <th scope="col">Week</th>
                <th scope="col">\(\Delta\)</th>
                <th scope="col">p &gt; 0</th>
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                  <td>No</td>
                  <td>0.98<br>[0.94–0.99]</td>
                  <td>0.93<br>[0.82–0.97]</td>
                  <td>2020-03-09</td>
                  <td>0.02<br>[0.01–0.05]</td>
                  <td>1.000</td>
                  <td>2021-06-28</td>
                  <td>0.07<br>[0.03–0.17]</td>
                  <td>1.000</td>
                </tr>
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                  <td>Yes</td>
                  <td>1.00<br>[0.99–1.00]</td>
                  <td>1.00<br>[0.98–1.00]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>0.91<br>[0.79–0.96]</td>
                  <td>0.89<br>[0.77–0.95]</td>
                  <td>2020-03-09</td>
                  <td>0.09<br>[0.04–0.20]</td>
                  <td>1.000</td>
                  <td>2021-06-28</td>
                  <td>0.11<br>[0.05–0.23]</td>
                  <td>1.000</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>1.00<br>[1.00–1.00]</td>
                  <td>1.00<br>[1.00–1.00]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>0.50<br>[0.34–0.67]</td>
                  <td>0.33<br>[0.20–0.50]</td>
                  <td>2020-03-09</td>
                  <td>0.23<br>[0.14–0.32]</td>
                  <td>1.000</td>
                  <td>2021-02-15</td>
                  <td>0.25<br>[0.15–0.34]</td>
                  <td>1.000</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>0.74<br>[0.57–0.86]</td>
                  <td>0.59<br>[0.39–0.76]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>0.66<br>[0.48–0.79]</td>
                  <td>0.18<br>[0.09–0.30]</td>
                  <td>2020-03-09</td>
                  <td>0.25<br>[0.16–0.37]</td>
                  <td>1.000</td>
                  <td>2021-02-08</td>
                  <td>0.39<br>[0.28–0.49]</td>
                  <td>1.000</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>0.91<br>[0.82–0.96]</td>
                  <td>0.54<br>[0.33–0.73]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>0.99<br>[0.92–1.00]</td>
                  <td>1.00<br>[1.00–1.00]</td>
                  <td>2021-06-28</td>
                  <td>0.00<br>[0.00–0.00]</td>
                  <td>0.999</td>
                  <td>2020-03-09</td>
                  <td>0.01<br>[0.00–0.08]</td>
                  <td>0.999</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>1.00<br>[1.00–1.00]</td>
                  <td>1.00<br>[1.00–1.00]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</figure>
</div>
</div>
</section>
<section id="complete-model-results" class="level3">
<h3 class="anchored" data-anchor-id="complete-model-results">Complete model results</h3>
<div class="cell" data-layout-align="center">
<div id="tbl-results-full-policies" class="cell quarto-float anchored" data-layout-align="center">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-results-full-policies-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;A&nbsp;2: Complete results from models showing relationship between derogations and emergency policies (H<sub>1</sub>)
</figcaption>
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Cancel Public Events</th>
                <th scope="col">Gathering Restrictions</th>
                <th scope="col">Close Public Transit</th>
                <th scope="col">Movement</th>
                <th scope="col">International Travel</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="6">Note: Estimates are median posterior log odds from ordered logistic and binary logistic regression models; 95% credible intervals (highest density posterior interval, or HDPI) in brackets. Total \(R^2\) considers the variance of both population and group effects; marginal \(R^2\) only takes population effects into account.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Derogation in effect            </td>
                  <td>3.4             </td>
                  <td>10.1             </td>
                  <td>1.04              </td>
                  <td>1.7             </td>
                  <td>7.10          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[1.3, 5.8]      </td>
                  <td>[2.8, 46.5]      </td>
                  <td>[0.64, 1.45]      </td>
                  <td>[1.2, 2.2]      </td>
                  <td>[-0.17, 34.82]</td>
                </tr>
                <tr>
                  <td>New cases (standardized)        </td>
                  <td>2.18            </td>
                  <td>8.5              </td>
                  <td>-0.67             </td>
                  <td>0.77            </td>
                  <td>6.37          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.77, 5.56]   </td>
                  <td>[5.6, 11.2]      </td>
                  <td>[-0.89, -0.48]    </td>
                  <td>[0.33, 1.22]    </td>
                  <td>[-0.91, 16.16]</td>
                </tr>
                <tr>
                  <td>Cumulative cases (standardized) </td>
                  <td>3.4             </td>
                  <td>4.7              </td>
                  <td>-0.384            </td>
                  <td>-0.20           </td>
                  <td>1.4           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[1.3, 5.5]      </td>
                  <td>[3.0, 6.9]       </td>
                  <td>[-0.805, 0.039]   </td>
                  <td>[-0.61, 0.19]   </td>
                  <td>[-6.3, 13.8]  </td>
                </tr>
                <tr>
                  <td>New deaths (standardized)       </td>
                  <td>8.2             </td>
                  <td>2.40             </td>
                  <td>1.24              </td>
                  <td>0.74            </td>
                  <td>-1.61         </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[5.0, 11.4]     </td>
                  <td>[0.95, 3.84]     </td>
                  <td>[0.96, 1.53]      </td>
                  <td>[0.39, 1.12]    </td>
                  <td>[-3.24, -0.28]</td>
                </tr>
                <tr>
                  <td>Cumulative deaths (standardized)</td>
                  <td>-0.931          </td>
                  <td>-2.7             </td>
                  <td>0.72              </td>
                  <td>0.20            </td>
                  <td>6.00          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.884, 0.032] </td>
                  <td>[-3.6, -1.9]     </td>
                  <td>[0.28, 1.14]      </td>
                  <td>[-0.23, 0.58]   </td>
                  <td>[0.75, 11.95] </td>
                </tr>
                <tr>
                  <td>Past ICCPR derogation           </td>
                  <td>0.44            </td>
                  <td>-1.30            </td>
                  <td>0.071             </td>
                  <td>0.14            </td>
                  <td>-1.2          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.80, 1.66]   </td>
                  <td>[-2.38, -0.26]   </td>
                  <td>[-0.648, 0.846]   </td>
                  <td>[-0.65, 0.86]   </td>
                  <td>[-3.6, 1.1]   </td>
                </tr>
                <tr>
                  <td>Past ICCPR action               </td>
                  <td>-0.086          </td>
                  <td>0.19             </td>
                  <td>-0.29             </td>
                  <td>0.062           </td>
                  <td>0.32          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.200, 0.988] </td>
                  <td>[-0.76, 1.28]    </td>
                  <td>[-1.01, 0.41]     </td>
                  <td>[-0.765, 0.875] </td>
                  <td>[-1.93, 2.44] </td>
                </tr>
                <tr>
                  <td>Rule of law                     </td>
                  <td>3.3             </td>
                  <td>0.7              </td>
                  <td>-0.70             </td>
                  <td>-0.80           </td>
                  <td>-0.11         </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[1.2, 5.5]      </td>
                  <td>[-1.1, 2.4]      </td>
                  <td>[-2.06, 0.63]     </td>
                  <td>[-2.23, 0.58]   </td>
                  <td>[-3.72, 3.71] </td>
                </tr>
                <tr>
                  <td>Civil liberties                 </td>
                  <td>-4.23           </td>
                  <td>1.3              </td>
                  <td>1.1               </td>
                  <td>-0.74           </td>
                  <td>0.89          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-7.83, -0.68]  </td>
                  <td>[-1.4, 4.3]      </td>
                  <td>[-1.2, 3.4]       </td>
                  <td>[-2.98, 1.66]   </td>
                  <td>[-4.56, 7.40] </td>
                </tr>
                <tr>
                  <td>Core civil society index        </td>
                  <td>0.41            </td>
                  <td>0.0014           </td>
                  <td>-0.86             </td>
                  <td>-0.51           </td>
                  <td>-1.8          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-2.08, 2.72]   </td>
                  <td>[-2.1084, 1.9654]</td>
                  <td>[-2.58, 0.80]     </td>
                  <td>[-2.20, 1.06]   </td>
                  <td>[-7.2, 2.6]   </td>
                </tr>
                <tr>
                  <td>Constant                        </td>
                  <td>8.0             </td>
                  <td>4.8              </td>
                  <td>1.29              </td>
                  <td>3.6             </td>
                  <td>11            </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[6.7, 9.6]      </td>
                  <td>[3.5, 6.0]       </td>
                  <td>[0.48, 2.14]      </td>
                  <td>[2.7, 4.5]      </td>
                  <td>[7, 15]       </td>
                </tr>
                <tr>
                  <td>Year-week                       </td>
                  <td>-0.021          </td>
                  <td>-0.0029          </td>
                  <td>-0.0103           </td>
                  <td>-0.032          </td>
                  <td>0.050         </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.026, -0.015]</td>
                  <td>[-0.0082, 0.0020]</td>
                  <td>[-0.0133, -0.0075]</td>
                  <td>[-0.036, -0.029]</td>
                  <td>[0.034, 0.071]</td>
                </tr>
                <tr>
                  <td>Country random effects σ        </td>
                  <td>2.5             </td>
                  <td>2.3              </td>
                  <td>1.8               </td>
                  <td>1.9             </td>
                  <td>3.9           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[2.0, 2.9]      </td>
                  <td>[2.0, 2.8]       </td>
                  <td>[1.5, 2.1]        </td>
                  <td>[1.7, 2.2]      </td>
                  <td>[2.6, 5.5]    </td>
                </tr>
                <tr>
                  <td>N                               </td>
                  <td>9453            </td>
                  <td>9522             </td>
                  <td>8832              </td>
                  <td>9246            </td>
                  <td>9591          </td>
                </tr>
                <tr>
                  <td>\(R^2\) (total)               </td>
                  <td>0.31            </td>
                  <td>0.41             </td>
                  <td>0.36              </td>
                  <td>0.40            </td>
                  <td>0.32          </td>
                </tr>
                <tr>
                  <td>\(R^2\) (marginal)            </td>
                  <td>0.01            </td>
                  <td>0.03             </td>
                  <td>0.07              </td>
                  <td>0.12            </td>
                  <td>0.00          </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</figure>
</div>
</div>
</section>
</section>
<section id="h2-derogations-and-human-rights" class="level2">
<h2 class="anchored" data-anchor-id="h2-derogations-and-human-rights">H<sub>2</sub>: Derogations and human rights</h2>
<section id="predicted-probabilities-and-group-contrasts-1" class="level3">
<h3 class="anchored" data-anchor-id="predicted-probabilities-and-group-contrasts-1">Predicted probabilities and group contrasts</h3>
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        <thead>
<tr>
<th scope="col" align="center" colspan="1"> </th>
<th scope="col" align="center" colspan="1"> </th>
<th scope="col" align="center" colspan="2">Predicted probabilities</th>
<th scope="col" align="center" colspan="3">Smallest difference</th>
<th scope="col" align="center" colspan="3">Largest difference</th>
</tr>
        </thead><caption></caption>
              <tbody><tr>
                <th scope="col">Derogation</th>
                <th scope="col">Level</th>
                <th scope="col">2020-Q2</th>
                <th scope="col">2021-Q2</th>
                <th scope="col">Week</th>
                <th scope="col">\(\Delta\)</th>
                <th scope="col">p &gt; 0</th>
                <th scope="col">Week</th>
                <th scope="col">\(\Delta\)</th>
                <th scope="col">p &gt; 0</th>
              </tr>
        
        
        </tbody><tbody>
                <tr>
                  <td>No</td>
                  <td>None</td>
                  <td>0.87<br>[0.79–0.93]</td>
                  <td>0.94<br>[0.89–0.97]</td>
                  <td>2021-Q2</td>
                  <td>0.02<br>[-0.04–0.06]</td>
                  <td>0.8113</td>
                  <td>2020-Q2</td>
                  <td>0.04<br>[-0.07–0.13]</td>
                  <td>0.8113</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>None</td>
                  <td>0.92<br>[0.78–0.98]</td>
                  <td>0.96<br>[0.89–0.99]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Minor</td>
                  <td>0.06<br>[0.03–0.10]</td>
                  <td>0.03<br>[0.02–0.05]</td>
                  <td>2020-Q2</td>
                  <td>-0.02<br>[-0.06–0.03]</td>
                  <td>0.1888</td>
                  <td>2021-Q2</td>
                  <td>-0.01<br>[-0.03–0.02]</td>
                  <td>0.1888</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Minor</td>
                  <td>0.04<br>[0.01–0.10]</td>
                  <td>0.02<br>[0.00–0.06]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Moderate</td>
                  <td>0.01<br>[0.01–0.02]</td>
                  <td>0.01<br>[0.00–0.01]</td>
                  <td>2020-Q2</td>
                  <td>0.00<br>[-0.01–0.01]</td>
                  <td>0.1888</td>
                  <td>2021-Q2</td>
                  <td>0.00<br>[-0.01–0.00]</td>
                  <td>0.1888</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Moderate</td>
                  <td>0.01<br>[0.00–0.02]</td>
                  <td>0.00<br>[0.00–0.01]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Major</td>
                  <td>0.05<br>[0.03–0.10]</td>
                  <td>0.02<br>[0.01–0.05]</td>
                  <td>2020-Q2</td>
                  <td>-0.02<br>[-0.06–0.03]</td>
                  <td>0.1888</td>
                  <td>2021-Q2</td>
                  <td>-0.01<br>[-0.03–0.02]</td>
                  <td>0.1888</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Major</td>
                  <td>0.03<br>[0.01–0.10]</td>
                  <td>0.01<br>[0.00–0.05]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>—</td>
                  <td>0.03<br>[0.01–0.06]</td>
                  <td>0.02<br>[0.01–0.04]</td>
                  <td>2021-Q2</td>
                  <td>0.00<br>[-0.02–0.04]</td>
                  <td>0.5248</td>
                  <td>2020-Q2</td>
                  <td>0.00<br>[-0.03–0.06]</td>
                  <td>0.5248</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>—</td>
                  <td>0.03<br>[0.01–0.10]</td>
                  <td>0.02<br>[0.00–0.07]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>None</td>
                  <td>0.66<br>[0.54–0.76]</td>
                  <td>0.61<br>[0.49–0.71]</td>
                  <td>2020-Q2</td>
                  <td>0.22<br>[0.11–0.33]</td>
                  <td>0.9995</td>
                  <td>2021-Q2</td>
                  <td>0.25<br>[0.13–0.37]</td>
                  <td>0.9995</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>None</td>
                  <td>0.89<br>[0.76–0.95]</td>
                  <td>0.86<br>[0.71–0.95]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Minor</td>
                  <td>0.02<br>[0.01–0.03]</td>
                  <td>0.02<br>[0.01–0.03]</td>
                  <td>2020-Q2</td>
                  <td>-0.01<br>[-0.02–0.00]</td>
                  <td>0.0005</td>
                  <td>2021-Q2</td>
                  <td>-0.01<br>[-0.02–0.00]</td>
                  <td>0.0005</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Minor</td>
                  <td>0.01<br>[0.00–0.02]</td>
                  <td>0.01<br>[0.00–0.02]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Moderate</td>
                  <td>0.32<br>[0.23–0.44]</td>
                  <td>0.37<br>[0.27–0.49]</td>
                  <td>2021-Q2</td>
                  <td>-0.24<br>[-0.36–-0.12]</td>
                  <td>0.0005</td>
                  <td>2020-Q2</td>
                  <td>-0.21<br>[-0.32–-0.11]</td>
                  <td>0.0005</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Moderate</td>
                  <td>0.11<br>[0.04–0.22]</td>
                  <td>0.13<br>[0.05–0.27]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>None</td>
                  <td>0.66<br>[0.55–0.75]</td>
                  <td>0.92<br>[0.88–0.95]</td>
                  <td>2020-Q2</td>
                  <td>-0.05<br>[-0.18–0.07]</td>
                  <td>0.2150</td>
                  <td>2021-Q2</td>
                  <td>-0.02<br>[-0.08–0.02]</td>
                  <td>0.2150</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>None</td>
                  <td>0.60<br>[0.44–0.75]</td>
                  <td>0.90<br>[0.82–0.95]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Minor</td>
                  <td>0.19<br>[0.15–0.24]</td>
                  <td>0.05<br>[0.03–0.08]</td>
                  <td>2021-Q2</td>
                  <td>0.01<br>[-0.01–0.05]</td>
                  <td>0.7850</td>
                  <td>2020-Q2</td>
                  <td>0.02<br>[-0.03–0.07]</td>
                  <td>0.7850</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Minor</td>
                  <td>0.21<br>[0.15–0.27]</td>
                  <td>0.06<br>[0.03–0.11]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Moderate</td>
                  <td>0.11<br>[0.07–0.16]</td>
                  <td>0.02<br>[0.01–0.04]</td>
                  <td>2021-Q2</td>
                  <td>0.00<br>[-0.01–0.03]</td>
                  <td>0.7850</td>
                  <td>2020-Q2</td>
                  <td>0.02<br>[-0.03–0.08]</td>
                  <td>0.7850</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Moderate</td>
                  <td>0.13<br>[0.07–0.22]</td>
                  <td>0.03<br>[0.01–0.05]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
                <tr>
                  <td>No</td>
                  <td>Major</td>
                  <td>0.04<br>[0.02–0.06]</td>
                  <td>0.01<br>[0.00–0.01]</td>
                  <td>2021-Q2</td>
                  <td>0.00<br>[0.00–0.01]</td>
                  <td>0.7850</td>
                  <td>2020-Q2</td>
                  <td>0.01<br>[-0.01–0.04]</td>
                  <td>0.7850</td>
                </tr>
                <tr>
                  <td>Yes</td>
                  <td>Major</td>
                  <td>0.05<br>[0.02–0.10]</td>
                  <td>0.01<br>[0.00–0.02]</td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                  <td></td>
                </tr>
        </tbody>
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    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</section>
<section id="complete-model-results-1" class="level3">
<h3 class="anchored" data-anchor-id="complete-model-results-1">Complete model results</h3>
<div class="cell" data-layout-align="center">
<div id="tbl-results-full-human-rights" class="cell quarto-float anchored" data-layout-align="center">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-results-full-human-rights-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;A&nbsp;3: Complete results from models showing relationship between derogations and human rights (H<sub>2</sub>)
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        <thead>
        
              <tr>
                <th scope="col"> </th>
                <th scope="col">Discriminatory Policy</th>
                <th scope="col">Non-Derogable Rights</th>
                <th scope="col">No Time Limit Measures</th>
                <th scope="col">Abusive Enforcement</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="5">Note: Estimates are median posterior log odds from ordered logistic and binary logistic regression models; 95% credible intervals (highest density posterior interval, or HDPI) in brackets. Total \(R^2\) considers the variance of both population and group effects; marginal \(R^2\) only takes population effects into account.</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Derogation in effect            </td>
                  <td>-0.50         </td>
                  <td>0.044          </td>
                  <td>-1.39          </td>
                  <td>0.22           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.71, 0.57] </td>
                  <td>[-1.215, 1.196]</td>
                  <td>[-2.25, -0.52] </td>
                  <td>[-0.33, 0.76]  </td>
                </tr>
                <tr>
                  <td>New cases (standardized)        </td>
                  <td>0.26          </td>
                  <td>0.051          </td>
                  <td>-0.10          </td>
                  <td>0.029          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.49, 1.14] </td>
                  <td>[-1.480, 1.613]</td>
                  <td>[-0.92, 0.75]  </td>
                  <td>[-0.524, 0.617]</td>
                </tr>
                <tr>
                  <td>Cumulative cases (standardized) </td>
                  <td>-0.11         </td>
                  <td>-0.1           </td>
                  <td>-0.51          </td>
                  <td>0.18           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.10, 0.93] </td>
                  <td>[-2.2, 1.6]    </td>
                  <td>[-1.50, 0.36]  </td>
                  <td>[-0.58, 0.91]  </td>
                </tr>
                <tr>
                  <td>New deaths (standardized)       </td>
                  <td>-0.28         </td>
                  <td>-0.074         </td>
                  <td>0.079          </td>
                  <td>0.21           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.20, 0.57] </td>
                  <td>[-1.359, 1.094]</td>
                  <td>[-0.469, 0.717]</td>
                  <td>[-0.33, 0.79]  </td>
                </tr>
                <tr>
                  <td>Cumulative deaths (standardized)</td>
                  <td>0.12          </td>
                  <td>-0.41          </td>
                  <td>0.28           </td>
                  <td>-0.29          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.95, 1.13] </td>
                  <td>[-2.06, 0.96]  </td>
                  <td>[-0.45, 1.03]  </td>
                  <td>[-1.00, 0.39]  </td>
                </tr>
                <tr>
                  <td>Past ICCPR derogation           </td>
                  <td>0.94          </td>
                  <td>0.37           </td>
                  <td>0.14           </td>
                  <td>0.457          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[0.42, 1.51]  </td>
                  <td>[-0.41, 1.07]  </td>
                  <td>[-0.33, 0.57]  </td>
                  <td>[0.035, 0.859] </td>
                </tr>
                <tr>
                  <td>Past ICCPR action               </td>
                  <td>0.27          </td>
                  <td>1.39           </td>
                  <td>-0.53          </td>
                  <td>0.033          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.26, 0.78] </td>
                  <td>[0.78, 2.04]   </td>
                  <td>[-0.95, -0.11] </td>
                  <td>[-0.356, 0.416]</td>
                </tr>
                <tr>
                  <td>Rule of law                     </td>
                  <td>1.02          </td>
                  <td>0.88           </td>
                  <td>0.71           </td>
                  <td>-0.73          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.33, 2.44] </td>
                  <td>[-0.93, 2.57]  </td>
                  <td>[-0.19, 1.72]  </td>
                  <td>[-1.67, 0.16]  </td>
                </tr>
                <tr>
                  <td>Civil liberties                 </td>
                  <td>1.2           </td>
                  <td>-3.97          </td>
                  <td>-1.40          </td>
                  <td>0.23           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-1.2, 3.7]   </td>
                  <td>[-7.40, -0.86] </td>
                  <td>[-3.31, 0.54]  </td>
                  <td>[-1.42, 2.01]  </td>
                </tr>
                <tr>
                  <td>Core civil society index        </td>
                  <td>-2.22         </td>
                  <td>0.64           </td>
                  <td>-0.27          </td>
                  <td>-0.30          </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-3.89, -0.67]</td>
                  <td>[-1.45, 2.83]  </td>
                  <td>[-1.57, 1.20]  </td>
                  <td>[-1.56, 0.95]  </td>
                </tr>
                <tr>
                  <td>Constant                        </td>
                  <td>              </td>
                  <td>-1.32          </td>
                  <td>               </td>
                  <td>               </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>              </td>
                  <td>[-2.62, -0.15] </td>
                  <td>               </td>
                  <td>               </td>
                </tr>
                <tr>
                  <td>Cut 1                           </td>
                  <td>1.3           </td>
                  <td>               </td>
                  <td>0.55           </td>
                  <td>-0.712         </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[-0.3, 2.7]   </td>
                  <td>               </td>
                  <td>[-0.54, 1.46]  </td>
                  <td>[-1.491, 0.098]</td>
                </tr>
                <tr>
                  <td>Cut 2                           </td>
                  <td>2.01          </td>
                  <td>               </td>
                  <td>0.63           </td>
                  <td>0.38           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[0.44, 3.42]  </td>
                  <td>               </td>
                  <td>[-0.42, 1.58]  </td>
                  <td>[-0.44, 1.15]  </td>
                </tr>
                <tr>
                  <td>Cut 3                           </td>
                  <td>2.24          </td>
                  <td>               </td>
                  <td>               </td>
                  <td>1.9            </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[0.64, 3.60]  </td>
                  <td>               </td>
                  <td>               </td>
                  <td>[1.0, 2.7]     </td>
                </tr>
                <tr>
                  <td>Region random effects σ         </td>
                  <td>1.28          </td>
                  <td>0.71           </td>
                  <td>0.85           </td>
                  <td>0.56           </td>
                </tr>
                <tr>
                  <td>                                </td>
                  <td>[0.54, 2.55]  </td>
                  <td>[0.20, 1.54]   </td>
                  <td>[0.31, 1.74]   </td>
                  <td>[0.21, 1.25]   </td>
                </tr>
                <tr>
                  <td>N                               </td>
                  <td>834           </td>
                  <td>834            </td>
                  <td>834            </td>
                  <td>834            </td>
                </tr>
                <tr>
                  <td>\(R^2\) (total)               </td>
                  <td>0.14          </td>
                  <td>0.11           </td>
                  <td>0.07           </td>
                  <td>0.15           </td>
                </tr>
                <tr>
                  <td>\(R^2\) (marginal)            </td>
                  <td>0.08          </td>
                  <td>0.09           </td>
                  <td>0.04           </td>
                  <td>0.10           </td>
                </tr>
        </tbody>
      </table>
    </div>
<!-- hack to avoid NA insertion in last line -->
</div>
</div>
</figure>
</div>
</div>
<div class="cell" data-layout-align="center">
<div id="tbl-who-regions" class="cell quarto-float anchored" data-layout-align="center">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-who-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;A&nbsp;4: WHO regions
</figcaption>
<div aria-describedby="tbl-who-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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                  <td>AFRO: Regional Office for Africa</td>
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                  <td>AMRO: Regional Office for the Americas</td>
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                <tr>
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<div class="cell" data-layout-align="center">
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Table&nbsp;A&nbsp;5: States that derogated from regional human rights conventions
</figcaption>
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                <th scope="col">States that derogated from the ACHR</th>
                <th scope="col">Also submitted ICCPR derogation</th>
                <th scope="col">States that derogated from the ECHR</th>
                <th scope="col">Also submitted ICCPR derogation</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="4">ACHR = American Convention on Human Rights; ECHR = European Convention on Human Rights</td></tr></tfoot>
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                  <td>Argentina</td>
                  <td>Yes</td>
                  <td>Albania</td>
                  <td>No</td>
                </tr>
                <tr>
                  <td>Bolivia</td>
                  <td>No</td>
                  <td>Armenia</td>
                  <td>Yes</td>
                </tr>
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                  <td>Yes</td>
                  <td>Azerbaijan</td>
                  <td>Yes</td>
                </tr>
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                  <td>Colombia</td>
                  <td>Yes</td>
                  <td>Estonia</td>
                  <td>Yes</td>
                </tr>
                <tr>
                  <td>Dominican Republic</td>
                  <td>Yes</td>
                  <td>Georgia</td>
                  <td>Yes</td>
                </tr>
                <tr>
                  <td>Ecuador</td>
                  <td>Yes</td>
                  <td>Latvia</td>
                  <td>Yes</td>
                </tr>
                <tr>
                  <td>El Salvador</td>
                  <td>Yes</td>
                  <td>Moldova</td>
                  <td>Yes</td>
                </tr>
                <tr>
                  <td>Guatemala</td>
                  <td>Yes</td>
                  <td>North Macedonia</td>
                  <td>No</td>
                </tr>
                <tr>
                  <td>Honduras</td>
                  <td>No</td>
                  <td>Romania</td>
                  <td>Yes</td>
                </tr>
                <tr>
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                  <td>No</td>
                  <td>San Marino</td>
                  <td>Yes</td>
                </tr>
                <tr>
                  <td>Panama</td>
                  <td>No</td>
                  <td>Serbia</td>
                  <td>No</td>
                </tr>
                <tr>
                  <td>Paraguay</td>
                  <td>Yes</td>
                  <td> </td>
                  <td> </td>
                </tr>
                <tr>
                  <td>Peru</td>
                  <td>Yes</td>
                  <td> </td>
                  <td> </td>
                </tr>
                <tr>
                  <td>Suriname</td>
                  <td>No</td>
                  <td> </td>
                  <td> </td>
                </tr>
                <tr>
                  <td>Venezuela</td>
                  <td>No</td>
                  <td> </td>
                  <td> </td>
                </tr>
        </tbody>
      </table>
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<div class="cell" data-layout-align="center">
<div id="tbl-results-determinants" class="cell quarto-float anchored" data-layout-align="center">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-results-determinants-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table&nbsp;A&nbsp;6: Results from <span class="citation" data-cites="ChaudhryComstockHeiss:2024">Chaudhry, Comstock, and Heiss (<a href="#ref-ChaudhryComstockHeiss:2024" role="doc-biblioref">2024</a>)</span> showing the determinants of derogation
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              <tr>
                <th scope="col"> </th>
                <th scope="col">Model 1</th>
                <th scope="col">Model 2</th>
              </tr>
        </thead>
        <tfoot><tr><td colspan="3">Dependent variable = derogation submission</td></tr>
<tr><td colspan="3">Logistic regression models; odds ratios (standard error)</td></tr>
<tr><td colspan="3">* p &lt; 0.1, ** p &lt; 0.05, *** p &lt; 0.01</td></tr></tfoot>
        <tbody>
                <tr>
                  <td>Intercept                                           </td>
                  <td>0.00217 (0.00093)***</td>
                  <td>0.0044 (0.0021)***</td>
                </tr>
                <tr>
                  <td>Pandemic backsliding (PanBack)                      </td>
                  <td>7.1 (6.5)**         </td>
                  <td>                  </td>
                </tr>
                <tr>
                  <td>Pandemic violations of democratic standards (PanDem)</td>
                  <td>                    </td>
                  <td>0.29 (0.29)       </td>
                </tr>
                <tr>
                  <td>New cases (standardized)                            </td>
                  <td>0.33 (0.40)         </td>
                  <td>0.42 (0.51)       </td>
                </tr>
                <tr>
                  <td>New deaths (standardized)                           </td>
                  <td>1.66 (0.44)*        </td>
                  <td>1.64 (0.43)*      </td>
                </tr>
                <tr>
                  <td>Cumulative cases (standardized)                     </td>
                  <td>0.015 (0.033)*      </td>
                  <td>0.0067 (0.0152)** </td>
                </tr>
                <tr>
                  <td>Cumulative deaths (standardized)                    </td>
                  <td>3.6 (1.7)***        </td>
                  <td>4.3 (2.1)***      </td>
                </tr>
                <tr>
                  <td>Rule of law index                                   </td>
                  <td>1.99 (0.87)         </td>
                  <td>1.27 (0.58)       </td>
                </tr>
                <tr>
                  <td>N                                                   </td>
                  <td>9591                </td>
                  <td>9591              </td>
                </tr>
        </tbody>
      </table>
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</div>
</section>
</section>
<section id="references" class="level2">


</section>

<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
<div id="ref-Burkner:2017" class="csl-entry" role="listitem">
Bürkner, Paul-Christian. 2017. <span>“<span class="nocase">brms</span>: An R Package for Bayesian Multilevel Models Using Stan.”</span> <em>Journal of Statistical Software</em> 80 (1): 1–28. <a href="https://doi.org/10.18637/jss.v080.i01">https://doi.org/10.18637/jss.v080.i01</a>.
</div>
<div id="ref-ChaudhryComstockHeiss:2024" class="csl-entry" role="listitem">
Chaudhry, Suparna, Audrey L. Comstock, and Andrew Heiss. 2024. <span>“Derogations, Democratic Backsliding, and International Human Rights During the COVID-19 Pandemic.”</span> Working paper. <a href="https://doi.org/10.31235/osf.io/g3z6n">https://doi.org/10.31235/osf.io/g3z6n</a>.
</div>
<div id="ref-GelmanJakulinPittau:2008" class="csl-entry" role="listitem">
Gelman, Andrew, Aleks Jakulin, Maria Grazia Pittau, and Yu-Sung Su. 2008. <span>“A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models.”</span> <em>The Annals of Applied Statistics</em> 2 (4). <a href="https://doi.org/10.1214/08-AOAS191">https://doi.org/10.1214/08-AOAS191</a>.
</div>
<div id="ref-rproject-4-3-3" class="csl-entry" role="listitem">
R Core Team. 2023. <span>“R: A Language and Environment for Statistical Computing.”</span> Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.r-project.org/">https://www.r-project.org/</a>.
</div>
<div id="ref-stan" class="csl-entry" role="listitem">
Stan Development Team. 2023. <span>“Stan Modeling Language Users Guide and Reference Manual.”</span> Manual. <a href="http://mc-stan.org">http://mc-stan.org</a>.
</div>
</div></section></div></main>
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});
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</div> <!-- /content -->




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